Final Report
Michael Puerto
11/27/2020
Using r/LivestreamFail to understand twitch streamers,chat and emotes.
1 Getting Started
Recently, twitch literature has begun characterizing twitch communities through twitch chat, viewership trends, and content but no known projects have used resources that exist outside of twitch to understand how twitch communities manifest and interact with one another.
One subreddit called LivestreamFail (LSF) is a dedicated subreddit where users share these twitch clips, general twitch news, and twitch drama.LivestreamFail is one way smaller streamers become noticed and is a platform that I can use to compare big and small communities. I’m interested in the ways emotes are used between smaller and larger communities because I believe emote meanings and sentiments are being actively redefined. This analysis will be split into an LSF part and Twitch emote-sentiment part.
This analysis will investigate users, posts, and comments (sentiment and topic) on r/LivestreamFail and then investigate the comment data and emote use from twitch clips that were featured in LSF posts.
Libraries used
- tidyverse
- tidytext
- tm
- lubridate
- stringr
- text2vec
- jsonlite
- widyr
- quanteda
- visNetwork
- igraph
- ggraph
- DT
- ggthemes
- extrafont
- BTM
2 r/LivestreamFail
Image credit: LSF Twitter
2.1 LSF Data Collection
Using Python, R and Bigquery
- Python
- used this to interact with PRAW (The Python Reddit API) that grabbed ‘current’ posts.
- This data is used in the emote(companion) analysis
- R
- Used to interact with BigqueryR to pull data from the Pushshift reddit dataset which contained historical data from ’16.
- Collected Reddit posts and Reddit Comments
This resulted in over 39k reddit posts and 1.4 M reddit comments. 900 posts were then used in R scrape links and document if the clips had chat available for download.
## Warning: 'stopwords(language = "SMART")' is deprecated.
## Use 'stopwords(source = "smart")' instead.
## See help("Deprecated")## Rows: 39,696
## Columns: 10
## $ title <chr> "PoE streamer gets scared b...
## $ score <dbl> 49, 62, 74, 402, 43, 631, 4...
## $ url <chr> "https://clips.twitch.tv/zi...
## $ date <date> 2016-10-23, 2016-10-01, 20...
## $ domain <chr> "clips.twitch.tv", "oddshot...
## $ author <chr> "spoonducky", "Chandman68",...
## $ permalink <chr> "/r/LivestreamFail/comments...
## $ titlev1 <chr> "PoE streamer scared no...
## $ titlev2 <chr> "PoE streamer scared no...
## $ titlev3 <chr> "PoE streamer scared notifi...lsf_data_comments = read_csv("C:/Users/macia/Documents/MSIA-19/Git/MSIA-21-/Network Analysis/lsf_data_comments.csv")
lsf_data_comments$date<-as.POSIXct(lsf_data_comments$date,origin = "1970-01-01")
glimpse(lsf_data_comments[2:1416070,])## Rows: 1,416,069
## Columns: 8
## $ body <chr> "He probably will be once his mom sees her charge history...
## $ name <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ link_id <chr> "t3_4jdlmz", "t3_4ltvda", "t3_4jv5uz", "t3_4keb97", "t3_4...
## $ date <dttm> 2016-05-14 22:51:40, 2016-05-31 04:09:14, 2016-05-18 10:...
## $ author <chr> "aleco247", "4LTRU15T1CD3M1G0D", "PM_ME_UR_WAIFU_NUDES", ...
## $ parent_id <chr> "t1_d35wc5v", "t1_d3q4mot", "t1_d3a173k", "t3_4keb97", "t...
## $ score <dbl> 111, 122, 71, 67, 110, 93, 51, 64, 71, 107, 112, 93, 220,...
## $ id <chr> "d35x91t", "d3q5t7v", "d3a8hjs", "d3edmln", "d2qkd00", "d...# I think we can link the posts to the comments using the posts perma link and the link id for the comments
# by removing the t*_ before the numbers on link_id
lsf_data$permalink[1]## [1] "/r/LivestreamFail/comments/58y5c3/poe_streamer_gets_scared_by_his_own_notification/"lsf_data_comments$link_id[1]## [1] "t3_4hha9f"rm(lsf_data_comments)2.2 Descriptions of lsf posts
This is a plot of “Karma” which can be thought of as likes over 3 years on the subreddit. As we can see, there is a growing interest in this subreddit since it’s recieved more than 4 times the upvotes in 2019 than in 2016.
lsf_data %>% mutate(year = as.factor(format(date, "%Y"))) %>% group_by(year) %>% summarize(sum(score)) %>% arrange(year)%>%ungroup()%>% ggplot(aes(x=year, y = `sum(score)`, group = 1))+geom_line()+labs(x = "Year", y ="Sum Score" , title ="Sum of Karma by Year",subtitle = "looks like we have increasing interest in LSF over the years") + theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green") + theme(plot.subtitle = element_text(size = 10))
But does this pattern hold true for every year?
lsf_data %>% mutate(month = as.factor(format(date, "%m")),
year = as.factor(year(date))) %>%
group_by(year,month) %>% summarize(sum(score)) %>% arrange(month)%>%ungroup()%>% ggplot(aes(x=month, y = `sum(score)`, group = 1))+geom_line()+
labs(x = "Month" , y = "Sum of Posts" , title = "Sum of Karma/Points by month")+ theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")+
facet_wrap(vars(year))
Yes there is a pattern at the monthly level! there are 2 clear peaks and dips. This information may be useful for streamers or community members to ‘farm’ clips or strategicly post on the reddit to maximize the oprotunity of karma or views.
This below will show the number of duplicate links that are posted on LSF.
lsf_data %>% group_by(url) %>% count(sort = T)%>% filter(n > 2) %>% ggplot(aes(x = url, y =n, fill = url)) + geom_col()+labs(x = "Link",y ="Number of Posts/Reposts",title ="Number of URL Reposts",subtitle = "Top 10 Links")+ theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")+theme(axis.text.x = element_blank(), legend.direction = "vertical", legend.title = element_blank(),axis.line.x = element_blank(),axis.ticks.x = element_blank() ) + guides(fill = guide_legend(ncol = 3))
which links are being posted on LSF overall?
library(lubridate)
lsf_data %>% group_by(domain, year(date)) %>% count() %>% filter(n>50) %>% ggplot(aes(x = `year(date)`, y = n , color = domain ))+
geom_line()+ theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")+ theme(legend.title = element_blank(),plot.subtitle = element_text(size = 10))+ labs(title = "Post Links over time.", subtitle = "Filtered for links occuring more than 50 times")
This next visualization helps me understand which types of media are featured on the subreddit. This is great becaues we can see that alot of these posts are Twitch related other than the etika post, who is a famous and beloved youtuber. Also, the keanu reeves moment during his presentation at a gaming convention.
library(ggrepel)
lsf_data %>% top_n(10,wt=score) %>% filter(score < 60000) %>% ggplot(aes(x = date, y = score, label = title, color = as.factor(score)))+
geom_point(show.legend = F)+
geom_label_repel(size = 3, vjust ="inward", hjust ='inward',show.legend = F, nudge_y = 10)+
theme_wsj(base_size = 10,color = "green",base_family = "Segoe Print")+
labs(title = "Top 10 upvoted posts by date",caption = "Twitch, youtube, games and memes")+ theme(plot.caption = element_text(size = 10)) 
2.2.1 User posts
2.2.1.1 Number of posts
Normiesree has a suspisous amount of posts! Could they be reposts? Are they legit posts? perhaps he’s the oldest surviving member of “top posters” on lsf, since many users were [deleted].
lsf_data %>% group_by(author) %>% filter(author != "[deleted]") %>% count(sort =T) %>% ungroup() %>% top_n(10) %>% ggplot(aes(x = reorder(author,n), y =n)) + geom_col()+
coord_flip()+labs(x = "Author",y ="Number of Posts",title ="Which users post the most?",subtitle = "Top 10 users")+ theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")
2.2.1.2 Top Karma’d users
lsf_data %>% group_by(author) %>% filter(author != "[deleted]") %>% summarize(sum(score)) %>%top_n(10) %>% ggplot(aes(x = reorder(author,`sum(score)`), y =`sum(score)`))+
geom_col()+
coord_flip()+
labs(y ="Total Score",x = "User",title = "Top upvoted redditors",subtitle = "Top 10 users")+
theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")
lsf_data %>%mutate(month = as.factor(format(date, "%m")),
year = as.factor(year(date))) %>% group_by(author,month, year) %>%
filter(author != "[deleted]") %>%
summarize("total score" = sum(score)) %>%
ungroup() %>%
filter(author %in% c("-eDgAR-","Normiesreeee69","bbranddon","moody420","TTVRaptor")) %>%
filter(year != c("2016","2017")) %>%
ggplot(aes(x = month, y =`total score`, color = author, group = author))+
geom_line(size = 3, alpha = 0.6)+
#labs(y ="Total Score",x = "User",title = "Top upvoted redditors",subtitle = "Top 10 users")+
theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")+
facet_wrap(vars(year), strip.position = "bottom")+
theme(legend.title = element_blank(),legend.position = "bottom")+
labs(title = "Reddit karma over time for popular users")
# This creates a !%in% kind of deal
`%notin%` <- Negate(`%in%`)
lsf_data %>%
filter(str_detect(titlev3, regex("xqc|xqcow|train|trainwrecks|trainwrecks|trainwreckstv|forsen|esfand|esfandtv|ludwig|mizkif", ignore_case = T))) %>%
mutate("streamer" = case_when(
str_detect(titlev3, regex("xqc|xqcow",ignore_case = T)) ~ "xQcOw",
str_detect(titlev3, regex("train|trainwrecks|trainwrecks|trainwreckstv",ignore_case = T)) ~ "TrainwrecksTv",
str_detect(titlev3, regex("forsen",ignore_case = T)) ~ "forsen",
str_detect(titlev3, regex("esfand|esfandtv",ignore_case = T)) ~ "EsfandTv",
str_detect(titlev3, regex("ludwig",ignore_case = T)) ~ "Ludwig",
str_detect(titlev3, regex("mizkif",ignore_case = T)) ~ "Mizkif")) %>%
group_by(streamer, ) %>% mutate(month = as.factor(format(date, "%m")),
year = as.factor(year(date))) %>%
group_by(year,month,streamer) %>% summarize("total score" = sum(score)) %>% ungroup() %>%
filter(year %notin% c("2016","2017")) %>%
ggplot(aes(x=month, y = `total score`, group = streamer,color = streamer))+geom_line(size = 2,alpha = 0.7)+
facet_wrap(vars(year),nrow = 1, ncol = 4,strip.position = "bottom") + theme_wsj(base_size = 9,base_family = "Segoe Print",color = "green")+ theme(legend.title = element_blank(),legend.position = "bottom")+
labs(title = "Karma over time for posts containing 'relevant' streamers")
3 Twitch
A glipmse at the data.
data <- read_csv("C:/Users/macia/Documents/MSIA-19/Git/Reddit-and-Twitch/Data Collection/chats.csv",col_types = cols(X1 = col_skip()))
head(data, n = 10)## # A tibble: 10 x 4
## body user date streamer
## <chr> <chr> <dttm> <chr>
## 1 +2 blakeleigh321 2017-01-04 23:24:33 Jerma985
## 2 OMEGALUL Humorous_Chimp 2013-03-29 16:35:36 Jerma985
## 3 OUT OF MOJO mayodongs 2019-10-02 15:48:12 Jerma985
## 4 DOOR OMEGALUL mprQQ 2016-01-05 14:07:25 Jerma985
## 5 OMEGALUL Clap zRaayan 2014-04-05 09:57:48 Jerma985
## 6 SPIDERMAN gum100 2011-03-11 18:28:18 Jerma985
## 7 +2 craigwhoisme 2019-12-17 21:08:26 Jerma985
## 8 OMEGALUL feralbutch 2019-01-30 19:27:07 Jerma985
## 9 LUL Angle_Dez 2014-07-23 23:43:32 Jerma985
## 10 OMEGALUL Pretendeer 2012-11-24 17:01:36 Jerma985glimpse(data)## Rows: 46,319
## Columns: 4
## $ body <chr> "+2", "OMEGALUL", "OUT OF MOJO", "DOOR OMEGALUL", "OMEGALU...
## $ user <chr> "blakeleigh321", "Humorous_Chimp", "mayodongs", "mprQQ", "...
## $ date <dttm> 2017-01-04 23:24:33, 2013-03-29 16:35:36, 2019-10-02 15:4...
## $ streamer <chr> "Jerma985", "Jerma985", "Jerma985", "Jerma985", "Jerma985"...3.1 Twitch: Data collection
tl;dr - twich dmca issues, streamer bans, and the app I used prevented me from downloading alot more data. Next time, I’ll work with the twitch api directly.
To actually download the twitch chat, I used links from the LSF analysis and the application by lay295/zigagrcar on github found here.
The Digital Millennium Copyright Act is affecting twitch in a big way.
Twitch is currenly in hot water with DMCA claims, and they are banning streamers for repeated streaming “copyrighted” songs. One method streamers use to combat this is by deleting their content shortly after it was broadcasted. This affected data collection since the collected twitch clips were being actively taken down.
This led to the collection of twitch chat from 227 links present in from the reddit posts.
3.2 Emote Data
R and Rselenium was used to scrape the emote data from FrankerFaceZ and BettertwitchTV. Roughly the Top 300 emotes used from each site was collected (emote name and link to image).
This is what a “busy” chat may look like. 
3.2.1 Emote data table
This is a data table with the names and images of the top 600 emotes from both sites (BTTV & FFZ) combined.
bttv emotes need to be updated, also not sure if gif emotes work.
# https://i.stack.imgur.com/kLMaS.jpg
test<-emote_data %>% mutate("emote_image" = paste("<img src=", emote_link, sep = "")) %>%
mutate(emote_image = paste0(emote_image,' height="52"></img>',sep = "")) %>% select(emote_name,emote_image)
datatable(test, escape = FALSE)3.3 Descriptions of twitch chat
This chart shows us how many unique chat lines there are per streamer. This metric is useful for understanding which streamers may be getting the most attention during a point in time on LSF (October in this case). This metric should later be controlled for clip length, since longer clips offer more opprotunity for chat engagement.
data %>% group_by(streamer) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x = reorder(streamer,-n), y = n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(size = 12, angle = 15,vjust = .55))+
labs(title = "Which streamer has the most chats?")
This visualization show us the most active twitch chatters in our dataset. In a larger dataset, finding those high-interactcion chatters maybe useful for drawing links between communities or even creating a contributer badges on twith (like the founders badge).
data %>% group_by(user) %>% filter(user %notin% c("StreamElements","Streamlabs","Nightbot")) %>% count(sort = T) %>%
head(n=10)%>%
ggplot(aes(x = reorder(user,-n), y = n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(size = 8, angle = 15,vjust = .55),
plot.title = element_text(size = 20))+
labs(title = "Which user has the most chats?")
# Streamelements and streamlabs are bots. This plot will give us further insight in the the demographics of the communities of top 5 streamers. This shows the number accounts created by year for each member of the chat by streamer. As an example, one conclusion that may be drawn is that streamers forsen and Mizkif are not attracting new accounts (New user/ban evaders) to their channels. Another conclusion that may be drawn is that Trainwreckstv in 2018, attracted alot of new users, and perhaps played a significant role in bringing new users to twitch. I should investigate futher to understand what happened with train in 2018. This was perhaps his drama year with MitchJones (A popular WOW streamer) or The Speech.
top_5_streamers <- data %>% group_by(streamer) %>% count(sort = T) %>% head(n=5) %>% distinct(streamer)
data %>% filter(streamer %in% top_5_streamers$streamer) %>% mutate(date_year = year(as.Date.character(date))) %>% group_by(date_year,streamer) %>% count(sort = T)%>%
ggplot(aes(x = date_year, y = n, color = streamer)) +
geom_line(size = 2)+
theme_wsj(base_size = 12, color = "green")+
labs(title = "Streamer Communities: Account Creation Dates", subtitle = "Top 5 Streamers")+
theme(plot.title = element_text(size = 15),plot.subtitle = element_text(size= 8),legend.title = element_blank(),legend.position = "bottom")
This next vizualization will show the total user chat broken down by streamer for users who were present in more than 4 different chats. I’ve already filtered out the popular chat bots, but this may be a means of learning about new chat bots that exists, or to uncover community moderators (also present in badges).
data %>%
add_count(user,streamer) %>%
filter(n>1,user %notin% c("StreamElements","Streamlabs","Nightbot","Fossabot")) %>%
group_by(user) %>%
add_count(n_distinct(streamer)) %>%
ungroup() %>%
top_n(`n_distinct(streamer)`, n = 2) %>%
arrange(desc(n)) %>%
filter(n >=20) %>%
add_count(user,streamer,name = "#_chat_per_clip") %>%
ggplot(aes(x = user, y = `#_chat_per_clip`, fill = streamer))+
geom_bar(position = "stack",stat = "identity") +
theme_wsj(base_size = 12, color = "green")+
theme(legend.title = element_blank(),legend.position = "bottom")+
labs(title = "multi-stream chatters")
3.3.1 Bag of words
Tokens, bigrams and trigrams can give us insign into popular emotes/words combinations and spams that occur in these chats.
3.3.1.1 Tokens
tokens %>% group_by(word) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(word,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25))+
labs(title ="Token Counts")
3.3.1.2 Bi-grams
data %>%
unnest_tokens(bigram,body,token = 'ngrams',n = 2)%>%
filter(str_detect(bigram,"^[:alpha:]")) %>%
group_by(bigram) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(bigram,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25,size = 9))+
labs(title ="Bigram Counts")
3.3.1.3 Tri-grams
data %>%
unnest_tokens(trigram,body,token = 'ngrams',n = 3)%>%
filter(str_detect(trigram,"^[:alpha:]")) %>%
group_by(trigram) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(trigram,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25,size = 9))+
labs(title ="trigram Counts")
As you can see, there are alot of duplicated words/phrases. I like to think of these as spams.
Some popular ones are “I was here Pogu I was here Pogu…” or “OMEGALUL OMEGALUL OMEGALUL OMEGALUL..”. One way to combat this is by stripping the chat text to only it’s unique words.
3.4 Comparisons of twitch chat
3.4.1 Word/Emote correlations
corpus_data <- readtext("C:/Users/macia/Documents/MSIA-19/Git/Reddit-and-Twitch/Data Collection/corpus_data.csv", text_field = 'text')
glimpse(corpus_data)## Rows: 35,303
## Columns: 3
## $ doc_id <chr> "corpus_data.csv.1", "corpus_data.csv.2", "corpus_data.csv...
## $ text <chr> "+2", "OMEGALUL", "OUT OF MOJO", "DOOR OMEGALUL", "OMEGALU...
## $ streamer <chr> "Jerma985", "Jerma985", "Jerma985", "Jerma985", "Jerma985"...corpus <- corpus(corpus_data)
dfm <- dfm(corpus, remove_punct=T)
# Select emotes
emotes = emote_data$emote_name
tags = dfm_select(dfm,pattern = emotes)
#tags
toptag = names(topfeatures(tags,30))
# These are the top emotes mentioned in the dataset from the list of popular bttv/ffz emotes
head(toptag)## [1] "omegalul" "pogu" "lulw" "kekw" "pepelaugh" "clap"3.4.2 Text Plot Networks
3.4.2.1 Emote Combinations
tag_fcm <- fcm(tags)
toptags_fcm <- fcm_select(tag_fcm, pattern = toptag)
textplot_network(toptags_fcm,min_freeq = 0.1, edge_alpha = 0.7, edge_size = 5)
3.4.2.2 Emote/word combinations
tags = dfm_select(dfm, pattern = c("£","â","<","ó","ðÿ"),selection = "remove")
#tags
toptag = names(topfeatures(tags,20))
tag_fcm <- fcm(tags)
toptags_fcm <- fcm_select(tag_fcm, pattern = toptag)
textplot_network(toptags_fcm,min_freeq = 0.1, edge_alpha = 0.7, edge_size = 5)
3.4.3 Directed Bi-gram network
The Previous plots don’t show us the direction of the combinations. This is useful to understand the order of emotes/spams. Otherwise, one may think that Clap EZ is an acceptable spam.
count_bigrams <- function(data) {
data %>%
unnest_tokens(bigram,"body", token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
count(word1, word2, sort = TRUE)
}
visualize_bigrams <- function(bigrams) {
set.seed(2020)
a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
bigrams %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a) +
geom_node_point(color = "lightblue", size = 5) +
geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
theme_void()
}
viz.bigrams <- data %>%
count_bigrams()
# filter out rare combinations, as well as digits and produce graph
viz.bigrams %>%
filter(n >70) %>%
visualize_bigrams()
3.4.4 Streamer emote comparisons
With this vizualization, I aimed to uncover the “shared” emotes between streamers/comunties. For example, we can see that the streamer EsfandTv shares alot of emotes with different streamers and may share many community members with TrainwrecksTv.
word_cors <- tokens %>%
group_by(word) %>%
filter(n() >= 10 ) %>%
pairwise_cor(word, streamer, sort = T)#, sort = TRUE)
top_10 <-word_cors %>% mutate("streamer" = case_when(
item2 == "trainwreckstv" ~ "trainwreckstv", # ahh,
item2 == "esfandtv" ~ "esfandtv",
item2 == "forsen" ~ "forsen",
item2 == "mizkif" ~ "mizkif",
item2 == "ludwig" ~ "ludwig",
item2 == "moonmoon" ~ "moonmoon",
item2 == "xqcow" ~ "xqcow",
item2 == "sykkuno" ~ "sykkuno",
item2 == "vadikus007" ~ "vadikus007",
item2 == "loltyler1" ~ "loltyler1",
TRUE ~ "WHO?"
)) # there are 83 unique streamers in the dataset, we should filter this some how. Either top 20, or maybe with chats > 100.
# Build a scraper that grabes the names of emotes for eache of the streamers?
#top_10 %>% group_by(streamer) %>% count() # strange numbers here, each streamer has same number?, because I filteer for top 10 above?
# from 2 mil rows
# to about 2k rows
test<-top_10 %>%
mutate(contains_emote = case_when(item1 %in% emote_data$emote_name ~ 1, TRUE ~ 0)) %>%
filter(contains_emote == 1) %>% # filtering for only emotes!
filter(streamer != "WHO?")%>%
group_by(streamer) %>% top_n(10,wt = correlation)
streamers = c("trainwreckstv","esfandtv","forsen","mizkif","ludwig","moonmoon","xqcow","sykkuno","vadikus007","loltyler1")
emote_data_1 <- emote_data %>% select(emote_name,emote_link) %>% na.omit()
# TEST 2
test_2 <- test %>% left_join(emote_data_1, by = c("item1" = "emote_name"))
#-------
test <- test %>% graph_from_data_frame()
test_viz <- toVisNetworkData(test)
test_viz$nodes <- test_viz$nodes %>% mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"))
#test_viz checking the dataframe
visNetwork(nodes = test_viz$nodes, edges = test_viz$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()#--------------------test_2 <- test_2 %>% graph_from_data_frame()
test_viz_2 <- toVisNetworkData(test_2)
test_viz_2$nodes <- test_viz_2$nodes %>% mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"),
"shape" = "image")
test_viz_2$nodes<-test_viz_2$nodes %>% left_join(test_viz_2$edges, by = c('id' = 'from')) %>% select(id,label,group,shape,emote_link) %>% rename(image = emote_link) %>% distinct(id, .keep_all = T)
test_viz_2$nodes<-test_viz_2$nodes %>% mutate(image = case_when(id == "xqcow" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/xqcow-profile_image-9298dca608632101-70x70.jpeg",
id == "loltyler1" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/f3591dbe4ee3d94b-profile_image-70x70.png",
id == "moonmoon" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/3973e918fe7cc8c8-profile_image-70x70.png",
id == "trainwreckstv" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/1f47965f-7961-4b64-ad6f-71808d7d7fe9-profile_image-70x70.png",
id == "forsen" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/forsen-profile_image-48b43e1e4f54b5c8-300x300.png",
id == "esfandtv" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/476ee93d-66a6-4e57-b3a9-db1ceb168ad8-profile_image-70x70.png",
id == "mizkif" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/ddd88d33-6c4f-424f-9246-5f4978c93148-profile_image-70x70.png",
id == "ludwig" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/bde8aaf5-35d4-4503-9797-842401da900f-profile_image-70x70.png",
id == "sykkuno" ~ "https://static-cdn.jtvnw.net/jtv_user_pictures/sykkuno-profile_image-6ab1e70e07e29e9b-70x70.jpeg",
TRUE ~ as.character(image))) %>% na.omit()
#test_viz_2 checking the dataframe
visNetwork(nodes = test_viz_2$nodes, edges = test_viz_2$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()#test_viz_2$nodes
# delete me at some point
test_viz$edges
test_viz$nodes
test_viz$nodes <- test_viz$nodes %>%
mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"),
"shape" = "image") %>%
inner_join(test_viz$edges, by = c("id" = "from"))# %>%
select(c(id,label,group,shape,emote_link))%>%
mutate("image" = magick::image_read(emote_link))
#test_viz checking the dataframe
visNetwork(nodes = test_viz$nodes, edges = test_viz$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()
2.3 Comments: Biterm Topic modeling for short texts.
not currently working